Gastos Casa
27 febrero, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 28/1/2023 | Netflix | 8320 | Tami | NA |
| 26/1/2023 | Comida | 10370 | Andrés | NA |
| 29/1/2023 | Comida | 59172 | Tami | NA |
| 31/1/2023 | Diosi | 23990 | Andrés | Arena diosi |
| 1/2/2023 | Comida | 6500 | Andrés | NA |
| 2/2/2023 | Comida | 4793 | Tami | NA |
| 2/2/2023 | Regalo Matri Chepa | 80000 | Tami | NA |
| 15/2/2023 | Comida | 68612 | Tami | NA |
| 15/2/2023 | Comida | 6800 | Andrés | NA |
| 19/2/2023 | Comida | 48617 | Tami | NA |
| 20/2/2023 | VTR | 21990 | Andrés | Entel hogar |
| 22/2/2023 | Diosi | 3600 | Andrés | Cobertor arena y juguete |
| 22/2/2023 | Transporte | 6700 | Andrés | NA |
| 22/2/2023 | Comida | 11220 | Andrés | Fika |
| 21/2/2023 | Comida | 11170 | Tami | NA |
| 22/2/2023 | Comida | 5969 | Andrés | NA |
| 22/2/2023 | Uber | 4407 | Tami | NA |
| 22/2/2023 | Uber | 6414 | Tami | NA |
| 22/2/2023 | Comida | 52690 | Tami | Restaurant Valpo |
| 22/2/2023 | Uber | 5215 | Tami | NA |
| 24/2/2023 | Uber | 8458 | Tami | NA |
| 24/2/2023 | Comida | 7300 | Tami | Helados Reñaca |
| 25/2/2023 | Uber | 2889 | Tami | NA |
| 26/2/2023 | Uber | 6876 | Tami | NA |
| 26/2/2023 | Enceres | 7500 | Andrés | Merval |
| 26/2/2023 | Comida | 68970 | Andrés | Il papparazzo |
| 26/2/2023 | Electricidad | 40440 | Andrés | Enel |
| 26/2/2023 | Comida | 18480 | Tami | Café Turri Valpo |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 5.5222e+08 2 5.8994 0.0029 **
## lag_depvar 8.0072e+10 1 1710.8431 <2e-16 ***
## Residuals 2.5741e+10 550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1040.228 13417.45 0.0171588
## 2-0 27522.895 21854.962 33190.83 0.0000000
## 2-1 20294.057 16902.451 23685.66 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 33 31622.57 0 27223.29
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## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 48415.00 2 38864.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 398 49757.16 15418.062
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 48415.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1998.89206 4031.80429 -529.53230 2445.47461 -2952.74524 524.88382
## 8 9 10 11 12 13
## -5646.82399 -1197.88658 -3977.26337 -439.70579 -4958.33988 -1641.08302
## 14 15 16 17 18 19
## -931.20500 348.70653 -3264.82967 -406.54489 -2154.58522 6577.16970
## 20 21 22 23 24 25
## -1528.03233 -1210.81014 1471.00766 -1183.68308 234.87370 1697.84614
## 26 27 28 29 30 31
## -7091.80996 933.67198 8185.52451 442.76524 10.89189 -2376.92898
## 32 33 34 35 36 37
## 1590.44971 4592.40991 1162.23706 2428.22448 -1825.52964 4640.50581
## 38 39 40 41 42 43
## 4322.97205 -2243.41938 -2960.96862 -1102.47677 -10738.45164 7254.67650
## 44 45 46 47 48 49
## 2552.49843 1371.75429 8114.37766 722.63207 6563.20640 6767.44154
## 50 51 52 53 54 55
## -5812.38171 -4754.62816 -5040.59699 -7929.30387 6101.70638 -4079.65512
## 56 57 58 59 60 61
## -4911.04290 3824.28522 873.85516 -41.10180 134.39914 -5002.63497
## 62 63 64 65 66 67
## 18104.09508 3684.16123 -3595.21973 5957.34459 7392.88394 14707.44303
## 68 69 70 71 72 73
## 1804.45587 -13109.27347 -1261.41364 4678.34671 -4853.40871 -4380.33367
## 74 75 76 77 78 79
## -10491.30227 2436.21405 -5417.58740 1029.84340 -6891.89874 501.09617
## 80 81 82 83 84 85
## -2392.41275 -2734.75752 -3976.60115 -589.85990 2267.31530 3729.55914
## 86 87 88 89 90 91
## 461.08934 -496.59888 184.53326 4292.19109 -1156.56010 1152.62386
## 92 93 94 95 96 97
## -2058.79986 -1046.29669 172.39573 271.02325 -7486.21718 2364.33556
## 98 99 100 101 102 103
## -8617.98863 -2983.31370 -4086.47645 -1791.20595 -1314.44047 3130.64559
## 104 105 106 107 108 109
## -2374.81924 2557.61460 -1181.10132 946.87607 2570.01725 -3160.25323
## 110 111 112 113 114 115
## -4738.78385 -879.95127 1874.91752 11675.30181 -1218.27113 2685.71543
## 116 117 118 119 120 121
## 4287.22869 3538.85170 -1056.06165 -4681.49547 -3709.55569 2319.98647
## 122 123 124 125 126 127
## -1724.23712 1342.10344 8864.57285 883.26997 165.21646 -2490.18027
## 128 129 130 131 132 133
## 2673.84038 7078.28783 1059.39971 -8454.65264 1759.37670 4150.62605
## 134 135 136 137 138 139
## -3136.34464 -1406.23938 -846.83065 -3876.46032 1173.08060 -499.89577
## 140 141 142 143 144 145
## -2918.93677 1703.74317 -1887.57533 -7841.21294 2002.43490 -3504.87879
## 146 147 148 149 150 151
## 2068.49082 -279.60563 1002.94947 -373.20251 1338.95782 1179.83986
## 152 153 154 155 156 157
## 3354.85091 -4851.65062 -1182.09552 -3246.31880 5936.63174 9749.65611
## 158 159 160 161 162 163
## -3203.58083 -4556.34902 3815.93330 426.43888 2933.30645 -5660.39569
## 164 165 166 167 168 169
## -6514.89877 4371.18637 17627.35364 3917.06695 -102.40077 -2156.01071
## 170 171 172 173 174 175
## -825.74382 3862.87119 53.03557 -7798.05936 3113.77853 4584.88667
## 176 177 178 179 180 181
## 897.37841 9021.89143 -8953.15477 -3209.47063 -10493.86733 -11021.29341
## 182 183 184 185 186 187
## 1424.41652 9494.70327 -1194.52403 6161.75687 6807.19889 13426.80878
## 188 189 190 191 192 193
## 8730.69662 -3750.69349 2752.98027 10653.82572 -1339.90534 -2157.31174
## 194 195 196 197 198 199
## -10009.37070 -6126.42007 1452.02953 -5009.91250 -9585.30521 5571.80852
## 200 201 202 203 204 205
## -2858.57227 -1506.88485 -598.60499 6702.08803 10108.64427 827.95304
## 206 207 208 209 210 211
## 3171.42078 3348.35532 6038.47810 13097.74470 -5397.14032 -11029.21393
## 212 213 214 215 216 217
## -5432.91863 -10368.17340 -4880.03443 1715.39573 -12811.01367 16562.27906
## 218 219 220 221 222 223
## 8031.17106 1769.60523 26931.14767 12828.75511 7654.32585 14348.43075
## 224 225 226 227 228 229
## -3573.84562 -1428.01855 4073.01325 654.69289 3032.60101 9290.60847
## 230 231 232 233 234 235
## 6132.65006 -1596.48475 -1532.17503 9709.21487 -11208.30170 -7020.98697
## 236 237 238 239 240 241
## -8300.93068 -9883.47179 3272.81710 1560.54436 -8081.45212 -8791.26638
## 242 243 244 245 246 247
## 9276.59529 -7553.93463 2683.39469 -10093.60938 -3867.24223 1606.44245
## 248 249 250 251 252 253
## 1197.11459 -12114.15029 3820.23038 2255.48731 4414.55808 2350.52297
## 254 255 256 257 258 259
## -938.69369 11358.58436 21124.79508 3479.95921 -3993.19846 4367.92459
## 260 261 262 263 264 265
## -1434.62360 3986.17322 -4600.38534 -10658.50295 -4519.97843 -322.16992
## 266 267 268 269 270 271
## -4987.52562 8969.77450 -4068.40301 4391.72420 -1895.82341 4636.70592
## 272 273 274 275 276 277
## 923.09527 7516.44807 -1187.08911 12242.86891 -4349.10195 1944.29006
## 278 279 280 281 282 283
## -154.83019 8064.05495 -4834.64997 -2521.51875 -11057.67363 -2483.51843
## 284 285 286 287 288 289
## 18840.01193 8001.29134 2960.15642 -403.94381 1124.29588 6613.58948
## 290 291 292 293 294 295
## 7103.41307 -18546.22577 -10942.75691 -7936.74508 9845.44274 3272.83323
## 296 297 298 299 300 301
## -971.41553 27609.53664 10307.61205 5148.24724 9763.03177 3105.30689
## 302 303 304 305 306 307
## -788.85473 8131.28226 -24056.86463 -3327.49834 31.13688 -6758.37980
## 308 309 310 311 312 313
## -3765.68278 3139.52635 -8976.36205 -3018.64735 -7970.98393 1779.88591
## 314 315 316 317 318 319
## -2927.97179 2273.69428 -3851.05787 27675.57539 -487.82393 3521.69375
## 320 321 322 323 324 325
## 11059.75322 5823.70202 32614.80472 5369.06059 -20682.52798 2022.87529
## 326 327 328 329 330 331
## 1340.20758 -6236.25653 -1511.64949 -33045.52716 1117.66649 -2053.29383
## 332 333 334 335 336 337
## 164.49730 -2901.54247 4356.93221 -157.75168 -6669.71817 -2833.91312
## 338 339 340 341 342 343
## -1906.19697 -7390.97283 4140.28155 -1077.40632 -1442.53711 -697.47246
## 344 345 346 347 348 349
## 474.34262 780.63660 -1319.00348 -9148.13962 -12915.91923 2600.18252
## 350 351 352 353 354 355
## -4028.87990 -3363.32045 -5683.43715 2047.58711 1683.44650 3052.53607
## 356 357 358 359 360 361
## -3467.97633 -220.84953 971.44747 7306.48282 568.41921 250.69118
## 362 363 364 365 366 367
## 2869.47309 -2465.59396 -594.02396 -8460.01813 -4340.95475 -5924.34871
## 368 369 370 371 372 373
## -4658.07016 -6957.68704 5313.04988 669.63239 7417.98963 -7339.88896
## 374 375 376 377 378 379
## -1966.28558 -3089.89087 -2167.36660 -12156.12583 2206.74140 -10328.80880
## 380 381 382 383 384 385
## 6003.42421 9642.79502 3428.82037 -2100.25036 1900.10876 7036.22431
## 386 387 388 389 390 391
## 11697.22564 -5525.23621 -5093.74751 107.39535 8825.56622 2071.90218
## 392 393 394 395 396 397
## 11473.99441 -9634.66646 3013.87226 949.09607 796.53511 -421.34024
## 398 399 400 401 402 403
## -331.44002 -14255.78212 8766.18806 -932.64428 -1120.11474 7237.91236
## 404 405 406 407 408 409
## -7677.34131 -1035.39375 -2264.61201 -5545.91723 -2577.37390 -3628.74285
## 410 411 412 413 414 415
## -8460.47512 6436.57994 1942.25962 -7072.49791 -7386.93559 14531.77514
## 416 417 418 419 420 421
## 4115.31148 4782.55925 -7753.65642 -4459.02590 -2311.33019 3113.62450
## 422 423 424 425 426 427
## -13717.45958 -2488.39575 -8791.00796 3328.61494 7292.24424 6884.70702
## 428 429 430 431 432 433
## -3685.41190 -3818.25048 -4417.51937 -1481.79802 -5402.53283 -6313.76922
## 434 435 436 437 438 439
## -5632.51690 -1072.24970 -525.51272 -4652.50746 2906.39288 5161.78030
## 440 441 442 443 444 445
## -4739.22358 -1839.86367 1894.79411 -3520.78929 3152.66200 -6263.75243
## 446 447 448 449 450 451
## -11793.93415 -4189.39326 9970.23776 -1710.33272 5076.40985 -5550.13582
## 452 453 454 455 456 457
## -801.70336 705.04066 3346.94141 -11949.73072 3690.96496 -6380.29038
## 458 459 460 461 462 463
## 6844.97927 3332.89086 2825.52674 -3529.59942 2408.80031 307.15724
## 464 465 466 467 468 469
## 2106.34921 -209.54442 3661.04577 -2330.69071 6113.98600 -6635.02182
## 470 471 472 473 474 475
## -2654.20536 -1892.30011 -4347.96396 3315.53955 8116.92540 -5699.56189
## 476 477 478 479 480 481
## 1803.29021 -5859.53174 -2522.83516 2335.47573 -12605.84741 -9428.85543
## 482 483 484 485 486 487
## -873.56715 350.97238 -630.28648 -1009.18392 -9251.57042 11429.33381
## 488 489 490 491 492 493
## 6568.74337 7751.78979 -5108.20973 5694.14867 9617.48601 6374.56124
## 494 495 496 497 498 499
## -13156.84953 -10249.04048 -3121.79857 -783.87983 -200.20443 -7300.08728
## 500 501 502 503 504 505
## 939.63484 4618.47821 5840.33138 991.58978 411.04338 -6909.12128
## 506 507 508 509 510 511
## 900.28524 -4717.29917 2164.44981 -963.80145 -7824.83922 -268.37722
## 512 513 514 515 516 517
## -2339.67472 -252.07739 1668.56177 -9158.53275 -7428.58160 24623.76560
## 518 519 520 521 522 523
## 10162.40361 6212.96523 -5006.39171 3124.10925 17342.16506 11792.32253
## 524 525 526 527 528 529
## -23835.95561 -4749.16368 -3421.98344 4884.76569 -43.56897 -10789.39682
## 530 531 532 533 534 535
## 4706.20449 14225.95362 -4661.32275 4687.61610 5867.37173 -1480.15909
## 536 537 538 539 540 541
## -4232.10249 -6765.07132 -1789.64756 8635.89720 441.70349 -7826.76068
## 542 543 544 545 546 547
## 2131.92223 -282.10905 684.88401 -10711.29987 -10740.34144 2364.86416
## 548 549 550 551 552 553
## 7330.43976 -989.26820 1165.88905 -7391.25831 8895.13586 1237.29842
## 554 555
## -11614.40663 8120.05415
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17270.39 20107.20 24345.68 24064.67 26409.46 23751.83 24465.54 19715.03
## 10 11 12 13 14 15 16 17
## 19452.55 16804.99 17579.63 14320.94 14371.92 15034.15 16724.54 15050.69
## 18 19 20 21 22 23 24 25
## 16081.59 15457.40 22514.03 21601.38 21083.14 22966.25 22294.70 22944.87
## 26 27 28 29 30 31 32 33
## 24784.10 18734.61 20454.48 28263.23 28320.68 27994.79 25632.84 27030.16
## 34 35 36 37 38 39 40 41
## 30859.19 31206.35 32610.39 30130.07 34120.03 37316.42 34383.25 31205.76
## 42 43 44 45 46 47 48 49
## 30057.74 20671.61 28162.93 30590.53 31675.77 38488.94 37985.37 42630.56
## 50 51 52 53 54 55 56 57
## 46851.38 39575.91 34164.17 29205.02 22374.44 28641.51 25234.61 21545.71
## 58 59 60 61 62 63 64 65
## 25938.00 27192.96 27488.89 27899.21 23785.19 40315.98 42153.22 37416.51
## 66 67 68 69 70 71 72 73
## 41608.12 46505.84 57135.12 55156.13 40453.13 37968.08 40974.98 35295.91
## 74 75 76 77 78 79 80 81
## 30764.73 21502.07 24691.87 20632.44 22710.90 17625.05 19633.13 18862.47
## 82 83 84 85 86 87 88 89
## 17893.74 15969.72 17242.83 20837.73 25239.34 26225.60 26250.47 26864.95
## 90 91 92 93 94 95 96 97
## 30974.99 29809.80 30805.51 28877.01 28079.75 28446.55 28851.65 22452.52
## 98 99 100 101 102 103 104 105
## 25456.56 18512.46 17372.76 15420.63 15719.30 16394.21 20850.53 19937.39
## 106 107 108 109 110 111 112 113
## 23435.67 23226.41 24896.41 27762.68 25269.93 21726.38 22000.80 24637.41
## 114 115 116 117 118 119 120 121
## 35462.27 33661.71 35492.49 38479.86 40428.63 38125.50 32965.41 29320.16
## 122 123 124 125 126 127 128 129
## 31395.38 29681.61 30858.86 38430.87 38074.64 37139.61 34014.59 35789.28
## 130 131 132 133 134 135 136 137
## 41167.46 40609.80 31843.62 33103.80 36281.92 32705.67 31098.83 30187.17
## 138 139 140 141 142 143 144 145
## 26756.78 28166.04 27936.51 25631.26 27648.29 26278.07 19903.57 22923.02
## 146 147 148 149 150 151 152 153
## 20757.65 23723.89 24261.91 25846.49 26027.90 27676.02 28972.01 31993.08
## 154 155 156 157 158 159 160 161
## 27479.81 26745.46 24309.65 30182.20 41224.01 39560.35 36934.92 41936.85
## 162 163 164 165 166 167 168 169
## 43340.26 46743.68 42226.18 37550.53 42955.93 59198.50 61402.54 59822.44
## 170 171 172 173 174 175 176 177
## 56659.74 55064.84 57757.54 56785.20 49105.51 51918.68 55647.62 55683.68
## 178 179 180 181 182 183 184 185
## 62786.44 53323.47 50086.30 40928.58 32498.87 35994.30 46060.81 45518.81
## 186 187 188 189 190 191 192 193
## 51449.80 57173.76 67917.30 73180.84 66898.59 67091.32 74135.76 69828.03
## 194 195 196 197 198 199 200 201
## 65367.23 54650.42 48702.40 50121.48 45732.31 37929.76 44331.00 42564.88
## 202 203 204 205 206 207 208 209
## 42204.18 42680.77 49449.93 58306.62 57937.58 59656.07 61305.81 65083.11
## 210 211 212 213 214 215 216 217
## 74515.00 66626.79 54859.06 49487.60 40516.89 37485.75 40588.01 30644.72
## 218 219 220 221 222 223 224 225
## 47556.11 54850.11 55748.71 78430.82 85898.39 87894.28 95457.85 86441.88
## 226 227 228 229 230 231 232 233
## 80462.27 80045.74 76707.97 75872.53 80592.21 81951.48 76407.32 71637.79
## 234 235 236 237 238 239 240 241
## 77270.73 63967.42 56033.07 48013.19 39655.47 43832.03 45976.88 39451.55
## 242 243 244 245 246 247 248 249
## 33154.26 43399.08 37667.03 41588.32 33880.53 32591.13 36233.03 39046.58
## 250 251 252 253 254 255 256 257
## 29909.63 35825.94 39613.44 44789.19 47497.55 46991.99 57255.20 74688.33
## 258 259 260 261 262 263 264 265
## 74504.06 67839.22 69315.62 65550.26 66991.10 60771.65 50085.55 46127.46
## 266 267 268 269 270 271 272 273
## 46336.10 42457.08 51228.97 47515.70 51647.25 49770.72 53823.19 54118.12
## 274 275 276 277 278 279 280 281
## 60113.52 57756.42 67393.96 61341.00 61550.26 59905.37 65627.22 59380.66
## 282 283 284 285 286 287 288 289
## 55957.10 45547.66 43950.27 61119.42 66629.27 67037.23 64464.28 63554.98
## 290 291 292 293 294 295 296 297
## 67541.30 71437.23 52503.33 42641.60 36674.56 46958.17 50188.13 49305.32
## 298 299 300 301 302 303 304 305
## 73413.10 79336.75 80001.97 84597.55 82802.71 77851.15 81305.29 56295.93
## 306 307 308 309 310 311 312 313
## 52570.72 52251.67 46064.54 43284.19 46874.36 39453.79 38180.56 32761.97
## 314 315 316 317 318 319 320 321
## 36532.69 35717.02 39534.49 37526.28 63218.40 61067.45 62685.10 70654.01
## 322 323 324 325 326 327 328 329
## 73032.62 98421.23 96804.81 72723.27 71525.51 69888.83 61869.94 59002.67
## 330 331 332 333 334 335 336 337
## 29060.76 32734.87 33172.79 35484.26 34827.50 40573.47 41645.15 36910.06
## 338 339 340 341 342 343 344 345
## 36127.34 36253.54 31589.58 37566.69 38227.68 38485.19 39357.80 41137.22
## 346 347 348 349 350 351 352 353
## 42952.57 42705.14 35675.49 26277.67 31602.88 30468.03 30059.58 27684.70
## 354 355 356 357 358 359 360 361
## 32346.55 36087.18 40534.55 38730.14 39985.84 42116.52 49484.87 50033.45
## 362 363 364 365 366 367 368 369
## 50234.38 52688.59 50181.17 49627.73 42299.67 39506.63 35697.50 33484.26
## 370 371 372 373 374 375 376 377
## 29556.38 36817.80 39096.44 46953.32 40946.86 40396.03 38938.65 38473.13
## 378 379 380 381 382 383 384 385
## 29373.97 33955.38 27032.29 35221.78 45517.32 49069.82 47349.46 49333.92
## 386 387 388 389 390 391 392 393
## 55531.49 64982.52 58218.46 52706.75 52436.43 59789.24 60310.72 68947.95
## 394 395 396 397 398 399 400 401
## 58093.13 59654.33 59216.04 58701.77 57194.15 55960.21 42766.81 51321.36
## 402 403 404 405 406 407 408 409
## 50325.40 49295.37 55673.48 48242.97 47556.61 45889.35 41582.23 40417.17
## 410 411 412 413 414 415 416 417
## 38488.05 32603.56 40447.88 43363.64 38055.22 33161.22 47979.12 51810.01
## 418 419 420 421 422 423 424 425
## 55725.08 48221.45 44558.04 43238.80 46812.32 35273.25 35003.44 29282.96
## 426 427 428 429 430 431 432 433
## 34852.61 43150.15 50017.41 46794.54 43873.81 40810.08 40698.68 37189.20
## 434 435 436 437 438 439 440 441
## 33341.52 30585.54 32155.94 33998.65 32010.46 36859.08 43042.22 39806.29
## 442 443 444 445 446 447 448 449
## 39513.35 42508.93 40402.62 44377.75 39641.79 30706.39 29548.05 40864.05
## 450 451 452 453 454 455 456 457
## 40546.73 46177.56 41829.42 42177.82 43792.49 47497.30 37408.04 42239.86
## 458 459 460 461 462 463 464 465
## 37679.59 45221.39 48728.76 51339.89 48081.20 50413.56 50614.37 52355.12
## 466 467 468 469 470 471 472 473
## 51854.53 54787.69 52125.59 57158.59 50442.78 48062.30 46653.54 43290.03
## 474 475 476 477 478 479 480 481
## 47032.65 54469.13 48916.14 50613.25 45420.84 43805.67 46628.42 36080.71
## 482 483 484 485 486 487 488 489
## 29665.42 31528.03 34215.00 35699.61 36662.00 30325.67 42810.83 49447.07
## 490 491 492 493 494 495 496 497
## 56252.78 50983.28 55798.94 63405.15 67202.85 53508.61 44120.37 42152.45
## 498 499 500 501 502 503 504 505
## 42474.49 43262.80 37769.37 40159.66 45442.10 51103.27 51810.39 51920.55
## 506 507 508 509 510 511 512 513
## 45645.14 46980.30 43252.98 45998.52 45665.41 39403.81 40530.82 39708.93
## 514 515 516 517 518 519 520 521
## 40810.58 43441.10 36307.01 31603.38 55407.02 63538.32 67178.11 60581.03
## 522 523 524 525 526 527 528 529
## 61915.69 75452.39 82403.96 57444.45 52332.98 49039.23 53402.43 52910.54
## 530 531 532 533 534 535 536 537
## 43129.51 48103.33 60718.18 55258.81 58644.20 62617.59 59680.82 54729.50
## 538 539 540 541 542 543 544 545
## 48215.36 46876.10 54784.58 54535.90 47122.79 49338.39 49165.69 49857.01
## 546 547 548 549 550 551 552 553
## 40539.77 32404.99 36731.13 44818.41 44616.11 46315.83 40347.29 49327.70
## 554 555
## 50478.84 40294.95
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8396
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.89944 0.5708352 3.255247
## t2* 1710.84311 26.4811895 233.301611
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.957402 6.034588 12.53788
## 2 lag_depvar 1381.690716 1723.426504 2141.53344Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Feb 27 00:39:04 2023
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## =-=-=-=-= Iteration 8000 Mon Feb 27 00:39:35 2023
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## =-=-=-=-= Iteration 10000 Mon Feb 27 00:39:43 2023
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## =-=-=-=-= Iteration 12000 Mon Feb 27 00:39:50 2023
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 0.000 | 5.410333 | 5.629750 | 6.874784 |
| Comida | 306.371 | 310.278417 | 314.087500 | 339.450676 |
| Comunicaciones | 0.000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 0.000 | 47.072333 | 38.297667 | 31.524297 |
| Enceres | 0.000 | 20.086417 | 17.443792 | 23.967432 |
| Farmacia | 0.000 | 1.831667 | 7.913875 | 9.685784 |
| Gas/Bencina | 70.300 | 44.325000 | 28.954333 | 26.278486 |
| Diosi | 23.990 | 31.180667 | 41.934250 | 39.888324 |
| donaciones/regalos | 0.000 | 0.000000 | 7.170083 | 7.424838 |
| Electrodomésticos/ Mantención casa | 0.000 | 3.944000 | 30.269500 | 22.418054 |
| VTR | 0.000 | 25.156667 | 22.121792 | 20.548324 |
| Netflix | 8.320 | 7.151583 | 7.090167 | 7.498676 |
| Otros | 0.000 | 3.151083 | 1.575542 | 1.021973 |
| Total | 408.981 | 499.588167 | 522.488250 | 536.581649 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1894, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-03-09 00:04:58 sería de: 36.878 pesos// Percentil 95% más alto proyectado: 40.709,42
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 35700.96 | 35678.65 |
| Lo.80 | 35801.88 | 35789.68 |
| Point.Forecast | 36878.16 | 38619.49 |
| Hi.80 | 38953.56 | 43214.60 |
| Hi.95 | 40099.08 | 45647.10 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3355 996.7419
## s.e. 0.1418 34.6664
##
## sigma^2 = 26892: log likelihood = -311.94
## AIC=629.87 AICc=630.42 BIC=635.49paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.3262 780.4780 7.1703
## s.e. 0.1432 435.8235 14.3957
##
## sigma^2 = 27354: log likelihood = -311.82
## AIC=631.63 AICc=632.56 BIC=639.12forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 725.2132 | 655.5615 | 697.3647 |
| Lo.80 | 843.9093 | 773.6558 | 779.0319 |
| Point.Forecast | 1068.1315 | 996.7414 | 960.2959 |
| Hi.80 | 1292.3537 | 1219.8269 | 1259.7297 |
| Hi.95 | 1411.0498 | 1337.9213 | 1454.3580 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Andrés | Tami |
|---|---|---|---|
| 1 | marzo_2019 | 68268 | 175533 |
| 2 | abril_2019 | 55031 | 152640 |
| 3 | mayo_2019 | 192219 | 152985 |
| 4 | junio_2019 | 84961 | 291067 |
| 5 | julio_2019 | 205893 | 241389 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.6
## [7] tidytext_0.4.1 DT_0.27 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.13.0
## [13] forecast_8.20 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.0 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.0 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.1.8 ggplot2_3.4.1 tidyverse_2.0.0
## [28] sjPlot_2.8.12 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.9 httr_1.4.5
## [34] readxl_1.4.2 zoo_1.8-11 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.4
## [10] fansi_1.0.4 ggfortify_0.4.15 magrittr_2.0.3
## [13] tzdb_0.3.0 modelr_0.1.10 vroom_1.6.1
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-53
## [19] colorspace_2.1-0 xfun_0.37 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-31 glue_1.6.2
## [25] r2d3_0.2.6 gtable_0.3.1 emmeans_1.8.4-1
## [28] sjstats_0.18.2 sjmisc_2.8.9 car_3.1-1
## [31] quantmod_0.4.20 abind_1.4-5 mvtnorm_1.1-3
## [34] DBI_1.1.3 ggeffects_1.2.0 Rcpp_1.0.10
## [37] viridisLite_0.4.1 xtable_1.8-4 performance_0.10.2
## [40] bit_4.0.5 datawizard_0.6.5 htmlwidgets_1.6.1
## [43] timeSeries_4021.105 gplots_3.1.3 ellipsis_0.3.2
## [46] spatial_7.3-14 pkgconfig_2.0.3 farver_2.1.1
## [49] nnet_7.3-16 sass_0.4.5 dbplyr_2.3.1
## [52] janitor_2.2.0 utf8_1.2.3 tidyselect_1.2.0
## [55] labeling_0.4.2 rlang_1.0.6 munsell_0.5.0
## [58] cellranger_1.1.0 tools_4.1.2 cachem_1.0.6
## [61] cli_3.6.0 generics_0.1.3 sjlabelled_1.2.0
## [64] broom_1.0.3 evaluate_0.20 fastmap_1.1.0
## [67] yaml_2.3.7 knitr_1.42 bit64_4.0.5
## [70] caTools_1.18.2 forge_0.2.0 nlme_3.1-153
## [73] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [76] compiler_4.1.2 rstudioapi_0.14 curl_5.0.0
## [79] bslib_0.4.2 highr_0.10 fBasics_4021.93
## [82] Matrix_1.5-3 its.analysis_1.6.0 nloptr_2.0.3
## [85] urca_1.3-3 vctrs_0.5.2 pillar_1.8.1
## [88] lifecycle_1.0.3 lmtest_0.9-40 jquerylib_0.1.4
## [91] estimability_1.4.1 bitops_1.0-7 insight_0.19.0
## [94] R6_2.5.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] codetools_0.2-18 gtools_3.9.4 boot_1.3-28
## [100] MASS_7.3-54 assertthat_0.2.1 rprojroot_2.0.3
## [103] withr_2.5.0 fracdiff_1.5-2 bayestestR_0.13.0
## [106] parallel_4.1.2 hms_1.1.2 quadprog_1.5-8
## [109] timeDate_4022.108 minqa_1.2.5 snakecase_0.11.0
## [112] rmarkdown_2.20 carData_3.0-5 TTR_0.24.3
## [115] base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))